{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6 - Attention is All You Need\n",
    "\n",
    "In this notebook we will be implementing a (slightly modified version) of the Transformer model from the [Attention is All You Need](https://arxiv.org/abs/1706.03762) paper. All images in this notebook will be taken from the Transformer paper. For more information about the Transformer, [see](https://www.mihaileric.com/posts/transformers-attention-in-disguise/) [these](https://jalammar.github.io/illustrated-transformer/) [three](http://nlp.seas.harvard.edu/2018/04/03/attention.html) articles.\n",
    "\n",
    "![](assets/transformer1.png)\n",
    "\n",
    "## Introduction\n",
    "\n",
    "Similar to the Convolutional Sequence-to-Sequence model, the Transformer does not use any recurrence. It also does not use any convolutional layers. Instead the model is entirely made up of linear layers, attention mechanisms and normalization. \n",
    "\n",
    "As of January 2020, Transformers are the dominant architecture in NLP and are used to achieve state-of-the-art results for many tasks and it appears as if they will be for the near future. \n",
    "\n",
    "The most popular Transformer variant is [BERT](https://arxiv.org/abs/1810.04805) (**B**idirectional **E**ncoder **R**epresentations from **T**ransformers) and pre-trained versions of BERT are commonly used to replace the embedding layers - if not more - in NLP models. \n",
    "\n",
    "A common library used when dealing with pre-trained transformers is the [Transformers](https://huggingface.co/transformers/) library, see [here](https://huggingface.co/transformers/pretrained_models.html) for a list of all pre-trained models available.\n",
    "\n",
    "The differences between the implementation in this notebook and the paper are:\n",
    "- we use a learned positional encoding instead of a static one\n",
    "- we use the standard Adam optimizer with a static learning rate instead of one with warm-up and cool-down steps\n",
    "- we do not use label smoothing\n",
    "\n",
    "We make all of these changes as they closely follow BERT's set-up and the majority of Transformer variants use a similar set-up."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the Data\n",
    "\n",
    "As always, let's import all the required modules and set the random seeds for reproducability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "import torchtext\n",
    "from torchtext.legacy.datasets import Multi30k\n",
    "from torchtext.legacy.data import Field, BucketIterator\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "import spacy\n",
    "import numpy as np\n",
    "\n",
    "import random\n",
    "import math\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 1234\n",
    "\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "torch.manual_seed(SEED)\n",
    "torch.cuda.manual_seed(SEED)\n",
    "torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll then create our tokenizers as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "spacy_de = spacy.load('de_core_news_sm')\n",
    "spacy_en = spacy.load('en_core_web_sm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize_de(text):\n",
    "    \"\"\"\n",
    "    Tokenizes German text from a string into a list of strings\n",
    "    \"\"\"\n",
    "    return [tok.text for tok in spacy_de.tokenizer(text)]\n",
    "\n",
    "def tokenize_en(text):\n",
    "    \"\"\"\n",
    "    Tokenizes English text from a string into a list of strings\n",
    "    \"\"\"\n",
    "    return [tok.text for tok in spacy_en.tokenizer(text)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our fields are the same as the previous notebook. The model expects data to be fed in with the batch dimension first, so we use `batch_first = True`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ben/miniconda3/envs/pytorch17/lib/python3.8/site-packages/torchtext-0.9.0a0+c38fd42-py3.8-linux-x86_64.egg/torchtext/data/field.py:150: UserWarning: Field class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.\n",
      "  warnings.warn('{} class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.'.format(self.__class__.__name__), UserWarning)\n"
     ]
    }
   ],
   "source": [
    "SRC = Field(tokenize = tokenize_de, \n",
    "            init_token = '<sos>', \n",
    "            eos_token = '<eos>', \n",
    "            lower = True, \n",
    "            batch_first = True)\n",
    "\n",
    "TRG = Field(tokenize = tokenize_en, \n",
    "            init_token = '<sos>', \n",
    "            eos_token = '<eos>', \n",
    "            lower = True, \n",
    "            batch_first = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then load the Multi30k dataset and build the vocabulary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ben/miniconda3/envs/pytorch17/lib/python3.8/site-packages/torchtext-0.9.0a0+c38fd42-py3.8-linux-x86_64.egg/torchtext/data/example.py:78: UserWarning: Example class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.\n",
      "  warnings.warn('Example class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.', UserWarning)\n"
     ]
    }
   ],
   "source": [
    "train_data, valid_data, test_data = Multi30k.splits(exts = ('.de', '.en'), \n",
    "                                                    fields = (SRC, TRG))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "SRC.build_vocab(train_data, min_freq = 2)\n",
    "TRG.build_vocab(train_data, min_freq = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we define the device and the data iterator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ben/miniconda3/envs/pytorch17/lib/python3.8/site-packages/torchtext-0.9.0a0+c38fd42-py3.8-linux-x86_64.egg/torchtext/data/iterator.py:48: UserWarning: BucketIterator class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.\n",
      "  warnings.warn('{} class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.'.format(self.__class__.__name__), UserWarning)\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 128\n",
    "\n",
    "train_iterator, valid_iterator, test_iterator = BucketIterator.splits(\n",
    "    (train_data, valid_data, test_data), \n",
    "     batch_size = BATCH_SIZE,\n",
    "     device = device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the Model\n",
    "\n",
    "Next, we'll build the model. Like previous notebooks it is made up of an *encoder* and a *decoder*, with the encoder *encoding* the input/source sentence (in German) into *context vector* and the decoder then *decoding* this context vector to output our output/target sentence (in English). \n",
    "\n",
    "### Encoder\n",
    "\n",
    "Similar to the ConvSeq2Seq model, the Transformer's encoder does not attempt to compress the entire source sentence, $X = (x_1, ... ,x_n)$, into a single context vector, $z$. Instead it produces a sequence of context vectors, $Z = (z_1, ... , z_n)$. So, if our input sequence was 5 tokens long we would have $Z = (z_1, z_2, z_3, z_4, z_5)$. Why do we call this a sequence of context vectors and not a sequence of hidden states? A hidden state at time $t$ in an RNN has only seen tokens $x_t$ and all the tokens before it. However, each context vector here has seen all tokens at all positions within the input sequence.\n",
    "\n",
    "![](assets/transformer-encoder.png)\n",
    "\n",
    "First, the tokens are passed through a standard embedding layer. Next, as the model has no recurrent it has no idea about the order of the tokens within the sequence. We solve this by using a second embedding layer called a *positional embedding layer*. This is a standard embedding layer where the input is not the token itself but the position of the token within the sequence, starting with the first token, the `<sos>` (start of sequence) token, in position 0. The position embedding has a \"vocabulary\" size of 100, which means our model can accept sentences up to 100 tokens long. This can be increased if we want to handle longer sentences.\n",
    "\n",
    "The original Transformer implementation from the Attention is All You Need paper does not learn positional embeddings. Instead it uses a fixed static embedding. Modern Transformer architectures, like BERT, use positional embeddings instead, hence we have decided to use them in these tutorials. Check out [this](http://nlp.seas.harvard.edu/2018/04/03/attention.html#positional-encoding) section to read more about the positional embeddings used in the original Transformer model.\n",
    "\n",
    "Next, the token and positional embeddings are elementwise summed together to get a vector which contains information about the token and also its position with in the sequence. However, before they are summed, the token embeddings are multiplied by a scaling factor which is $\\sqrt{d_{model}}$, where $d_{model}$ is the hidden dimension size, `hid_dim`. This supposedly reduces variance in the embeddings and the model is difficult to train reliably without this scaling factor. Dropout is then applied to the combined embeddings.\n",
    "\n",
    "The combined embeddings are then passed through $N$ *encoder layers* to get $Z$, which is then output and can be used by the decoder.\n",
    "\n",
    "The source mask, `src_mask`, is simply the same shape as the source sentence but has a value of 1 when the token in the source sentence is not a `<pad>` token and 0 when it is a `<pad>` token. This is used in the encoder layers to mask the multi-head attention mechanisms, which are used to calculate and apply attention over the source sentence, so the model does not pay attention to `<pad>` tokens, which contain no useful information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Encoder(nn.Module):\n",
    "    def __init__(self, \n",
    "                 input_dim, \n",
    "                 hid_dim, \n",
    "                 n_layers, \n",
    "                 n_heads, \n",
    "                 pf_dim,\n",
    "                 dropout, \n",
    "                 device,\n",
    "                 max_length = 100):\n",
    "        super().__init__()\n",
    "\n",
    "        self.device = device\n",
    "        \n",
    "        self.tok_embedding = nn.Embedding(input_dim, hid_dim)\n",
    "        self.pos_embedding = nn.Embedding(max_length, hid_dim)\n",
    "        \n",
    "        self.layers = nn.ModuleList([EncoderLayer(hid_dim, \n",
    "                                                  n_heads, \n",
    "                                                  pf_dim,\n",
    "                                                  dropout, \n",
    "                                                  device) \n",
    "                                     for _ in range(n_layers)])\n",
    "        \n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "        self.scale = torch.sqrt(torch.FloatTensor([hid_dim])).to(device)\n",
    "        \n",
    "    def forward(self, src, src_mask):\n",
    "        \n",
    "        #src = [batch size, src len]\n",
    "        #src_mask = [batch size, 1, 1, src len]\n",
    "        \n",
    "        batch_size = src.shape[0]\n",
    "        src_len = src.shape[1]\n",
    "        \n",
    "        pos = torch.arange(0, src_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)\n",
    "        \n",
    "        #pos = [batch size, src len]\n",
    "        \n",
    "        src = self.dropout((self.tok_embedding(src) * self.scale) + self.pos_embedding(pos))\n",
    "        \n",
    "        #src = [batch size, src len, hid dim]\n",
    "        \n",
    "        for layer in self.layers:\n",
    "            src = layer(src, src_mask)\n",
    "            \n",
    "        #src = [batch size, src len, hid dim]\n",
    "            \n",
    "        return src"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoder Layer\n",
    "\n",
    "The encoder layers are where all of the \"meat\" of the encoder is contained. We first pass the source sentence and its mask into the *multi-head attention layer*, then perform dropout on it, apply a residual connection and pass it through a [Layer Normalization](https://arxiv.org/abs/1607.06450) layer. We then pass it through a *position-wise feedforward* layer and then, again, apply dropout, a residual connection and then layer normalization to get the output of this layer which is fed into the next layer. The parameters are not shared between layers. \n",
    "\n",
    "The mutli head attention layer is used by the encoder layer to attend to the source sentence, i.e. it is calculating and applying attention over itself instead of another sequence, hence we call it *self attention*.\n",
    "\n",
    "[This](https://mlexplained.com/2018/01/13/weight-normalization-and-layer-normalization-explained-normalization-in-deep-learning-part-2/) article goes into more detail about layer normalization, but the gist is that it normalizes the values of the features, i.e. across the hidden dimension, so each feature has a mean of 0 and a standard deviation of 1. This allows neural networks with a larger number of layers, like the Transformer, to be trained easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EncoderLayer(nn.Module):\n",
    "    def __init__(self, \n",
    "                 hid_dim, \n",
    "                 n_heads, \n",
    "                 pf_dim,  \n",
    "                 dropout, \n",
    "                 device):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.self_attn_layer_norm = nn.LayerNorm(hid_dim)\n",
    "        self.ff_layer_norm = nn.LayerNorm(hid_dim)\n",
    "        self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)\n",
    "        self.positionwise_feedforward = PositionwiseFeedforwardLayer(hid_dim, \n",
    "                                                                     pf_dim, \n",
    "                                                                     dropout)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "    def forward(self, src, src_mask):\n",
    "        \n",
    "        #src = [batch size, src len, hid dim]\n",
    "        #src_mask = [batch size, 1, 1, src len] \n",
    "                \n",
    "        #self attention\n",
    "        _src, _ = self.self_attention(src, src, src, src_mask)\n",
    "        \n",
    "        #dropout, residual connection and layer norm\n",
    "        src = self.self_attn_layer_norm(src + self.dropout(_src))\n",
    "        \n",
    "        #src = [batch size, src len, hid dim]\n",
    "        \n",
    "        #positionwise feedforward\n",
    "        _src = self.positionwise_feedforward(src)\n",
    "        \n",
    "        #dropout, residual and layer norm\n",
    "        src = self.ff_layer_norm(src + self.dropout(_src))\n",
    "        \n",
    "        #src = [batch size, src len, hid dim]\n",
    "        \n",
    "        return src"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutli Head Attention Layer\n",
    "\n",
    "One of the key, novel concepts introduced by the Transformer paper is the *multi-head attention layer*. \n",
    "\n",
    "![](assets/transformer-attention.png)\n",
    "\n",
    "Attention can be though of as *queries*, *keys* and *values* - where the query is used with the key to get an attention vector (usually the output of a *softmax* operation and has all values between 0 and 1 which sum to 1) which is then used to get a weighted sum of the values.\n",
    "\n",
    "The Transformer uses *scaled dot-product attention*, where the query and key are combined by taking the dot product between them, then applying the softmax operation and scaling by $d_k$ before finally then multiplying by the value. $d_k$ is the *head dimension*, `head_dim`, which we will shortly explain further.\n",
    "\n",
    "$$ \\text{Attention}(Q, K, V) = \\text{Softmax} \\big( \\frac{QK^T}{\\sqrt{d_k}} \\big)V $$ \n",
    "\n",
    "This is similar to standard *dot product attention* but is scaled by $d_k$, which the paper states is used to stop the results of the dot products growing large, causing gradients to become too small.\n",
    "\n",
    "However, the scaled dot-product attention isn't simply applied to the queries, keys and values. Instead of doing a single attention application the queries, keys and values have their `hid_dim` split into $h$ *heads* and the scaled dot-product attention is calculated over all heads in parallel. This means instead of paying attention to one concept per attention application, we pay attention to $h$. We then re-combine the heads into their `hid_dim` shape, thus each `hid_dim` is potentially paying attention to $h$ different concepts.\n",
    "\n",
    "$$ \\text{MultiHead}(Q, K, V) = \\text{Concat}(\\text{head}_1,...,\\text{head}_h)W^O $$\n",
    "\n",
    "$$\\text{head}_i = \\text{Attention}(QW_i^Q, KW_i^K, VW_i^V) $$\n",
    "\n",
    "$W^O$ is the linear layer applied at the end of the multi-head attention layer, `fc`. $W^Q, W^K, W^V$ are the linear layers `fc_q`, `fc_k` and `fc_v`.\n",
    "\n",
    "Walking through the module, first we calculate $QW^Q$, $KW^K$ and $VW^V$ with the linear layers, `fc_q`, `fc_k` and `fc_v`, to give us `Q`, `K` and `V`. Next, we split the `hid_dim` of the query, key and value into `n_heads` using `.view` and correctly permute them so they can be multiplied together. We then calculate the `energy` (the un-normalized attention) by multiplying `Q` and `K` together and scaling it by the square root of `head_dim`, which is calulated as `hid_dim // n_heads`. We then mask the energy so we do not pay attention over any elements of the sequeuence we shouldn't, then apply the softmax and dropout. We then apply the attention to the value heads, `V`, before combining the `n_heads` together. Finally, we multiply this $W^O$, represented by `fc_o`. \n",
    "\n",
    "Note that in our implementation the lengths of the keys and values are always the same, thus when matrix multiplying the output of the softmax, `attention`, with `V` we will always have valid dimension sizes for matrix multiplication. This multiplication is carried out using `torch.matmul` which, when both tensors are >2-dimensional, does a batched matrix multiplication over the last two dimensions of each tensor. This will be a **[query len, key len] x [value len, head dim]** batched matrix multiplication over the batch size and each head which provides the **[batch size, n heads, query len, head dim]** result.\n",
    "\n",
    "One thing that looks strange at first is that dropout is applied directly to the attention. This means that our attention vector will most probably not sum to 1 and we may pay full attention to a token but the attention over that token is set to 0 by dropout. This is never explained, or even mentioned, in the paper however is used by the [official implementation](https://github.com/tensorflow/tensor2tensor/) and every Transformer implementation since, [including BERT](https://github.com/google-research/bert/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiHeadAttentionLayer(nn.Module):\n",
    "    def __init__(self, hid_dim, n_heads, dropout, device):\n",
    "        super().__init__()\n",
    "        \n",
    "        assert hid_dim % n_heads == 0\n",
    "        \n",
    "        self.hid_dim = hid_dim\n",
    "        self.n_heads = n_heads\n",
    "        self.head_dim = hid_dim // n_heads\n",
    "        \n",
    "        self.fc_q = nn.Linear(hid_dim, hid_dim)\n",
    "        self.fc_k = nn.Linear(hid_dim, hid_dim)\n",
    "        self.fc_v = nn.Linear(hid_dim, hid_dim)\n",
    "        \n",
    "        self.fc_o = nn.Linear(hid_dim, hid_dim)\n",
    "        \n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "        self.scale = torch.sqrt(torch.FloatTensor([self.head_dim])).to(device)\n",
    "        \n",
    "    def forward(self, query, key, value, mask = None):\n",
    "        \n",
    "        batch_size = query.shape[0]\n",
    "        \n",
    "        #query = [batch size, query len, hid dim]\n",
    "        #key = [batch size, key len, hid dim]\n",
    "        #value = [batch size, value len, hid dim]\n",
    "                \n",
    "        Q = self.fc_q(query)\n",
    "        K = self.fc_k(key)\n",
    "        V = self.fc_v(value)\n",
    "        \n",
    "        #Q = [batch size, query len, hid dim]\n",
    "        #K = [batch size, key len, hid dim]\n",
    "        #V = [batch size, value len, hid dim]\n",
    "                \n",
    "        Q = Q.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)\n",
    "        K = K.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)\n",
    "        V = V.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)\n",
    "        \n",
    "        #Q = [batch size, n heads, query len, head dim]\n",
    "        #K = [batch size, n heads, key len, head dim]\n",
    "        #V = [batch size, n heads, value len, head dim]\n",
    "                \n",
    "        energy = torch.matmul(Q, K.permute(0, 1, 3, 2)) / self.scale\n",
    "        \n",
    "        #energy = [batch size, n heads, query len, key len]\n",
    "        \n",
    "        if mask is not None:\n",
    "            energy = energy.masked_fill(mask == 0, -1e10)\n",
    "        \n",
    "        attention = torch.softmax(energy, dim = -1)\n",
    "                \n",
    "        #attention = [batch size, n heads, query len, key len]\n",
    "                \n",
    "        x = torch.matmul(self.dropout(attention), V)\n",
    "        \n",
    "        #x = [batch size, n heads, query len, head dim]\n",
    "        \n",
    "        x = x.permute(0, 2, 1, 3).contiguous()\n",
    "        \n",
    "        #x = [batch size, query len, n heads, head dim]\n",
    "        \n",
    "        x = x.view(batch_size, -1, self.hid_dim)\n",
    "        \n",
    "        #x = [batch size, query len, hid dim]\n",
    "        \n",
    "        x = self.fc_o(x)\n",
    "        \n",
    "        #x = [batch size, query len, hid dim]\n",
    "        \n",
    "        return x, attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Position-wise Feedforward Layer\n",
    "\n",
    "The other main block inside the encoder layer is the *position-wise feedforward layer* This is relatively simple compared to the multi-head attention layer. The input is transformed from `hid_dim` to `pf_dim`, where `pf_dim` is usually a lot larger than `hid_dim`. The original Transformer used a `hid_dim` of 512 and a `pf_dim` of 2048. The ReLU activation function and dropout are applied before it is transformed back into a `hid_dim` representation. \n",
    "\n",
    "Why is this used? Unfortunately, it is never explained in the paper.\n",
    "\n",
    "BERT uses the [GELU](https://arxiv.org/abs/1606.08415) activation function, which can be used by simply switching `torch.relu` for `F.gelu`. Why did they use GELU? Again, it is never explained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PositionwiseFeedforwardLayer(nn.Module):\n",
    "    def __init__(self, hid_dim, pf_dim, dropout):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.fc_1 = nn.Linear(hid_dim, pf_dim)\n",
    "        self.fc_2 = nn.Linear(pf_dim, hid_dim)\n",
    "        \n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        \n",
    "        #x = [batch size, seq len, hid dim]\n",
    "        \n",
    "        x = self.dropout(torch.relu(self.fc_1(x)))\n",
    "        \n",
    "        #x = [batch size, seq len, pf dim]\n",
    "        \n",
    "        x = self.fc_2(x)\n",
    "        \n",
    "        #x = [batch size, seq len, hid dim]\n",
    "        \n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decoder\n",
    "\n",
    "The objective of the decoder is to take the encoded representation of the source sentence, $Z$, and convert it into predicted tokens in the target sentence, $\\hat{Y}$. We then compare $\\hat{Y}$ with the actual tokens in the target sentence, $Y$, to calculate our loss, which will be used to calculate the gradients of our parameters and then use our optimizer to update our weights in order to improve our predictions. \n",
    "\n",
    "![](assets/transformer-decoder.png)\n",
    "\n",
    "The decoder is similar to encoder, however it now has two multi-head attention layers. A *masked multi-head attention layer* over the target sequence, and a multi-head attention layer which uses the decoder representation as the query and the encoder representation as the key and value.\n",
    "\n",
    "The decoder uses positional embeddings and combines - via an elementwise sum - them with the scaled embedded target tokens, followed by dropout. Again, our positional encodings have a \"vocabulary\" of 100, which means they can accept sequences up to 100 tokens long. This can be increased if desired.\n",
    "\n",
    "The combined embeddings are then passed through the $N$ decoder layers, along with the encoded source, `enc_src`, and the source and target masks. Note that the number of layers in the encoder does not have to be equal to the number of layers in the decoder, even though they are both denoted by $N$.\n",
    "\n",
    "The decoder representation after the $N^{th}$ layer is then passed through a linear layer, `fc_out`. In PyTorch, the softmax operation is contained within our loss function, so we do not explicitly need to use a softmax layer here.\n",
    "\n",
    "As well as using the source mask, as we did in the encoder to prevent our model attending to `<pad>` tokens, we also use a target mask. This will be explained further in the `Seq2Seq` model which encapsulates both the encoder and decoder, but the gist of it is that it performs a similar operation as the decoder padding in the convolutional sequence-to-sequence model. As we are processing all of the target tokens at once in parallel we need a method of stopping the decoder from \"cheating\" by simply \"looking\" at what the next token in the target sequence is and outputting it. \n",
    "\n",
    "Our decoder layer also outputs the normalized attention values so we can later plot them to see what our model is actually paying attention to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Decoder(nn.Module):\n",
    "    def __init__(self, \n",
    "                 output_dim, \n",
    "                 hid_dim, \n",
    "                 n_layers, \n",
    "                 n_heads, \n",
    "                 pf_dim, \n",
    "                 dropout, \n",
    "                 device,\n",
    "                 max_length = 100):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.device = device\n",
    "        \n",
    "        self.tok_embedding = nn.Embedding(output_dim, hid_dim)\n",
    "        self.pos_embedding = nn.Embedding(max_length, hid_dim)\n",
    "        \n",
    "        self.layers = nn.ModuleList([DecoderLayer(hid_dim, \n",
    "                                                  n_heads, \n",
    "                                                  pf_dim, \n",
    "                                                  dropout, \n",
    "                                                  device)\n",
    "                                     for _ in range(n_layers)])\n",
    "        \n",
    "        self.fc_out = nn.Linear(hid_dim, output_dim)\n",
    "        \n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "        self.scale = torch.sqrt(torch.FloatTensor([hid_dim])).to(device)\n",
    "        \n",
    "    def forward(self, trg, enc_src, trg_mask, src_mask):\n",
    "        \n",
    "        #trg = [batch size, trg len]\n",
    "        #enc_src = [batch size, src len, hid dim]\n",
    "        #trg_mask = [batch size, 1, trg len, trg len]\n",
    "        #src_mask = [batch size, 1, 1, src len]\n",
    "                \n",
    "        batch_size = trg.shape[0]\n",
    "        trg_len = trg.shape[1]\n",
    "        \n",
    "        pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)\n",
    "                            \n",
    "        #pos = [batch size, trg len]\n",
    "            \n",
    "        trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos))\n",
    "                \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "        \n",
    "        for layer in self.layers:\n",
    "            trg, attention = layer(trg, enc_src, trg_mask, src_mask)\n",
    "        \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "        #attention = [batch size, n heads, trg len, src len]\n",
    "        \n",
    "        output = self.fc_out(trg)\n",
    "        \n",
    "        #output = [batch size, trg len, output dim]\n",
    "            \n",
    "        return output, attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decoder Layer\n",
    "\n",
    "As mentioned previously, the decoder layer is similar to the encoder layer except that it now has two multi-head attention layers, `self_attention` and `encoder_attention`. \n",
    "\n",
    "The first performs self-attention, as in the encoder, by using the decoder representation so far as the query, key and value. This is followed by dropout, residual connection and layer normalization. This `self_attention` layer uses the target sequence mask, `trg_mask`, in order to prevent the decoder from \"cheating\" by paying attention to tokens that are \"ahead\" of the one it is currently processing as it processes all tokens in the target sentence in parallel.\n",
    "\n",
    "The second is how we actually feed the encoded source sentence, `enc_src`, into our decoder. In this multi-head attention layer the queries are the decoder representations and the keys and values are the encoder representations. Here, the source mask, `src_mask` is used to prevent the multi-head attention layer from attending to `<pad>` tokens within the source sentence. This is then followed by the dropout, residual connection and layer normalization layers. \n",
    "\n",
    "Finally, we pass this through the position-wise feedforward layer and yet another sequence of dropout, residual connection and layer normalization.\n",
    "\n",
    "The decoder layer isn't introducing any new concepts, just using the same set of layers as the encoder in a slightly different way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DecoderLayer(nn.Module):\n",
    "    def __init__(self, \n",
    "                 hid_dim, \n",
    "                 n_heads, \n",
    "                 pf_dim, \n",
    "                 dropout, \n",
    "                 device):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.self_attn_layer_norm = nn.LayerNorm(hid_dim)\n",
    "        self.enc_attn_layer_norm = nn.LayerNorm(hid_dim)\n",
    "        self.ff_layer_norm = nn.LayerNorm(hid_dim)\n",
    "        self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)\n",
    "        self.encoder_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)\n",
    "        self.positionwise_feedforward = PositionwiseFeedforwardLayer(hid_dim, \n",
    "                                                                     pf_dim, \n",
    "                                                                     dropout)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "    def forward(self, trg, enc_src, trg_mask, src_mask):\n",
    "        \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "        #enc_src = [batch size, src len, hid dim]\n",
    "        #trg_mask = [batch size, 1, trg len, trg len]\n",
    "        #src_mask = [batch size, 1, 1, src len]\n",
    "        \n",
    "        #self attention\n",
    "        _trg, _ = self.self_attention(trg, trg, trg, trg_mask)\n",
    "        \n",
    "        #dropout, residual connection and layer norm\n",
    "        trg = self.self_attn_layer_norm(trg + self.dropout(_trg))\n",
    "            \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "            \n",
    "        #encoder attention\n",
    "        _trg, attention = self.encoder_attention(trg, enc_src, enc_src, src_mask)\n",
    "        \n",
    "        #dropout, residual connection and layer norm\n",
    "        trg = self.enc_attn_layer_norm(trg + self.dropout(_trg))\n",
    "                    \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "        \n",
    "        #positionwise feedforward\n",
    "        _trg = self.positionwise_feedforward(trg)\n",
    "        \n",
    "        #dropout, residual and layer norm\n",
    "        trg = self.ff_layer_norm(trg + self.dropout(_trg))\n",
    "        \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "        #attention = [batch size, n heads, trg len, src len]\n",
    "        \n",
    "        return trg, attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seq2Seq\n",
    "\n",
    "Finally, we have the `Seq2Seq` module which encapsulates the encoder and decoder, as well as handling the creation of the masks.\n",
    "\n",
    "The source mask is created by checking where the source sequence is not equal to a `<pad>` token. It is 1 where the token is not a `<pad>` token and 0 when it is. It is then unsqueezed so it can be correctly broadcast when applying the mask to the `energy`, which of shape **_[batch size, n heads, seq len, seq len]_**.\n",
    "\n",
    "The target mask is slightly more complicated. First, we create a mask for the `<pad>` tokens, as we did for the source mask. Next, we create a \"subsequent\" mask, `trg_sub_mask`, using `torch.tril`. This creates a diagonal matrix where the elements above the diagonal will be zero and the elements below the diagonal will be set to whatever the input tensor is. In this case, the input tensor will be a tensor filled with ones. So this means our `trg_sub_mask` will look something like this (for a target with 5 tokens):\n",
    "\n",
    "$$\\begin{matrix}\n",
    "1 & 0 & 0 & 0 & 0\\\\\n",
    "1 & 1 & 0 & 0 & 0\\\\\n",
    "1 & 1 & 1 & 0 & 0\\\\\n",
    "1 & 1 & 1 & 1 & 0\\\\\n",
    "1 & 1 & 1 & 1 & 1\\\\\n",
    "\\end{matrix}$$\n",
    "\n",
    "This shows what each target token (row) is allowed to look at (column). The first target token has a mask of **_[1, 0, 0, 0, 0]_** which means it can only look at the first target token. The second target token has a mask of **_[1, 1, 0, 0, 0]_** which it means it can look at both the first and second target tokens. \n",
    "\n",
    "The \"subsequent\" mask is then logically anded with the padding mask, this combines the two masks ensuring both the subsequent tokens and the padding tokens cannot be attended to. For example if the last two tokens were `<pad>` tokens the mask would look like:\n",
    "\n",
    "$$\\begin{matrix}\n",
    "1 & 0 & 0 & 0 & 0\\\\\n",
    "1 & 1 & 0 & 0 & 0\\\\\n",
    "1 & 1 & 1 & 0 & 0\\\\\n",
    "1 & 1 & 1 & 0 & 0\\\\\n",
    "1 & 1 & 1 & 0 & 0\\\\\n",
    "\\end{matrix}$$\n",
    "\n",
    "After the masks are created, they used with the encoder and decoder along with the source and target sentences to get our predicted target sentence, `output`, along with the decoder's attention over the source sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Seq2Seq(nn.Module):\n",
    "    def __init__(self, \n",
    "                 encoder, \n",
    "                 decoder, \n",
    "                 src_pad_idx, \n",
    "                 trg_pad_idx, \n",
    "                 device):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "        self.src_pad_idx = src_pad_idx\n",
    "        self.trg_pad_idx = trg_pad_idx\n",
    "        self.device = device\n",
    "        \n",
    "    def make_src_mask(self, src):\n",
    "        \n",
    "        #src = [batch size, src len]\n",
    "        \n",
    "        src_mask = (src != self.src_pad_idx).unsqueeze(1).unsqueeze(2)\n",
    "\n",
    "        #src_mask = [batch size, 1, 1, src len]\n",
    "\n",
    "        return src_mask\n",
    "    \n",
    "    def make_trg_mask(self, trg):\n",
    "        \n",
    "        #trg = [batch size, trg len]\n",
    "        \n",
    "        trg_pad_mask = (trg != self.trg_pad_idx).unsqueeze(1).unsqueeze(2)\n",
    "        \n",
    "        #trg_pad_mask = [batch size, 1, 1, trg len]\n",
    "        \n",
    "        trg_len = trg.shape[1]\n",
    "        \n",
    "        trg_sub_mask = torch.tril(torch.ones((trg_len, trg_len), device = self.device)).bool()\n",
    "        \n",
    "        #trg_sub_mask = [trg len, trg len]\n",
    "            \n",
    "        trg_mask = trg_pad_mask & trg_sub_mask\n",
    "        \n",
    "        #trg_mask = [batch size, 1, trg len, trg len]\n",
    "        \n",
    "        return trg_mask\n",
    "\n",
    "    def forward(self, src, trg):\n",
    "        \n",
    "        #src = [batch size, src len]\n",
    "        #trg = [batch size, trg len]\n",
    "                \n",
    "        src_mask = self.make_src_mask(src)\n",
    "        trg_mask = self.make_trg_mask(trg)\n",
    "        \n",
    "        #src_mask = [batch size, 1, 1, src len]\n",
    "        #trg_mask = [batch size, 1, trg len, trg len]\n",
    "        \n",
    "        enc_src = self.encoder(src, src_mask)\n",
    "        \n",
    "        #enc_src = [batch size, src len, hid dim]\n",
    "                \n",
    "        output, attention = self.decoder(trg, enc_src, trg_mask, src_mask)\n",
    "        \n",
    "        #output = [batch size, trg len, output dim]\n",
    "        #attention = [batch size, n heads, trg len, src len]\n",
    "        \n",
    "        return output, attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Seq2Seq Model\n",
    "\n",
    "We can now define our encoder and decoders. This model is significantly smaller than Transformers used in research today, but is able to be run on a single GPU quickly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUT_DIM = len(SRC.vocab)\n",
    "OUTPUT_DIM = len(TRG.vocab)\n",
    "HID_DIM = 256\n",
    "ENC_LAYERS = 3\n",
    "DEC_LAYERS = 3\n",
    "ENC_HEADS = 8\n",
    "DEC_HEADS = 8\n",
    "ENC_PF_DIM = 512\n",
    "DEC_PF_DIM = 512\n",
    "ENC_DROPOUT = 0.1\n",
    "DEC_DROPOUT = 0.1\n",
    "\n",
    "enc = Encoder(INPUT_DIM, \n",
    "              HID_DIM, \n",
    "              ENC_LAYERS, \n",
    "              ENC_HEADS, \n",
    "              ENC_PF_DIM, \n",
    "              ENC_DROPOUT, \n",
    "              device)\n",
    "\n",
    "dec = Decoder(OUTPUT_DIM, \n",
    "              HID_DIM, \n",
    "              DEC_LAYERS, \n",
    "              DEC_HEADS, \n",
    "              DEC_PF_DIM, \n",
    "              DEC_DROPOUT, \n",
    "              device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, use them to define our whole sequence-to-sequence encapsulating model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "SRC_PAD_IDX = SRC.vocab.stoi[SRC.pad_token]\n",
    "TRG_PAD_IDX = TRG.vocab.stoi[TRG.pad_token]\n",
    "\n",
    "model = Seq2Seq(enc, dec, SRC_PAD_IDX, TRG_PAD_IDX, device).to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check the number of parameters, noticing it is significantly less than the 37M for the convolutional sequence-to-sequence model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model has 9,038,341 trainable parameters\n"
     ]
    }
   ],
   "source": [
    "def count_parameters(model):\n",
    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "\n",
    "print(f'The model has {count_parameters(model):,} trainable parameters')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The paper does not mention which weight initialization scheme was used, however Xavier uniform seems to be common amongst Transformer models, so we use it here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_weights(m):\n",
    "    if hasattr(m, 'weight') and m.weight.dim() > 1:\n",
    "        nn.init.xavier_uniform_(m.weight.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.apply(initialize_weights);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The optimizer used in the original Transformer paper uses Adam with a learning rate that has a \"warm-up\" and then a \"cool-down\" period. BERT and other Transformer models use Adam with a fixed learning rate, so we will implement that. Check [this](http://nlp.seas.harvard.edu/2018/04/03/attention.html#optimizer) link for more details about the original Transformer's learning rate schedule.\n",
    "\n",
    "Note that the learning rate needs to be lower than the default used by Adam or else learning is unstable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "LEARNING_RATE = 0.0005\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = LEARNING_RATE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we define our loss function, making sure to ignore losses calculated over `<pad>` tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss(ignore_index = TRG_PAD_IDX)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we'll define our training loop. This is the exact same as the one used in the previous tutorial.\n",
    "\n",
    "As we want our model to predict the `<eos>` token but not have it be an input into our model we simply slice the `<eos>` token off the end of the sequence. Thus:\n",
    "\n",
    "$$\\begin{align*}\n",
    "\\text{trg} &= [sos, x_1, x_2, x_3, eos]\\\\\n",
    "\\text{trg[:-1]} &= [sos, x_1, x_2, x_3]\n",
    "\\end{align*}$$\n",
    "\n",
    "$x_i$ denotes actual target sequence element. We then feed this into the model to get a predicted sequence that should hopefully predict the `<eos>` token:\n",
    "\n",
    "$$\\begin{align*}\n",
    "\\text{output} &= [y_1, y_2, y_3, eos]\n",
    "\\end{align*}$$\n",
    "\n",
    "$y_i$ denotes predicted target sequence element. We then calculate our loss using the original `trg` tensor with the `<sos>` token sliced off the front, leaving the `<eos>` token:\n",
    "\n",
    "$$\\begin{align*}\n",
    "\\text{output} &= [y_1, y_2, y_3, eos]\\\\\n",
    "\\text{trg[1:]} &= [x_1, x_2, x_3, eos]\n",
    "\\end{align*}$$\n",
    "\n",
    "We then calculate our losses and update our parameters as is standard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, iterator, optimizer, criterion, clip):\n",
    "    \n",
    "    model.train()\n",
    "    \n",
    "    epoch_loss = 0\n",
    "    \n",
    "    for i, batch in enumerate(iterator):\n",
    "        \n",
    "        src = batch.src\n",
    "        trg = batch.trg\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        output, _ = model(src, trg[:,:-1])\n",
    "                \n",
    "        #output = [batch size, trg len - 1, output dim]\n",
    "        #trg = [batch size, trg len]\n",
    "            \n",
    "        output_dim = output.shape[-1]\n",
    "            \n",
    "        output = output.contiguous().view(-1, output_dim)\n",
    "        trg = trg[:,1:].contiguous().view(-1)\n",
    "                \n",
    "        #output = [batch size * trg len - 1, output dim]\n",
    "        #trg = [batch size * trg len - 1]\n",
    "            \n",
    "        loss = criterion(output, trg)\n",
    "        \n",
    "        loss.backward()\n",
    "        \n",
    "        torch.nn.utils.clip_grad_norm_(model.parameters(), clip)\n",
    "        \n",
    "        optimizer.step()\n",
    "        \n",
    "        epoch_loss += loss.item()\n",
    "        \n",
    "    return epoch_loss / len(iterator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The evaluation loop is the same as the training loop, just without the gradient calculations and parameter updates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, iterator, criterion):\n",
    "    \n",
    "    model.eval()\n",
    "    \n",
    "    epoch_loss = 0\n",
    "    \n",
    "    with torch.no_grad():\n",
    "    \n",
    "        for i, batch in enumerate(iterator):\n",
    "\n",
    "            src = batch.src\n",
    "            trg = batch.trg\n",
    "\n",
    "            output, _ = model(src, trg[:,:-1])\n",
    "            \n",
    "            #output = [batch size, trg len - 1, output dim]\n",
    "            #trg = [batch size, trg len]\n",
    "            \n",
    "            output_dim = output.shape[-1]\n",
    "            \n",
    "            output = output.contiguous().view(-1, output_dim)\n",
    "            trg = trg[:,1:].contiguous().view(-1)\n",
    "            \n",
    "            #output = [batch size * trg len - 1, output dim]\n",
    "            #trg = [batch size * trg len - 1]\n",
    "            \n",
    "            loss = criterion(output, trg)\n",
    "\n",
    "            epoch_loss += loss.item()\n",
    "        \n",
    "    return epoch_loss / len(iterator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then define a small function that we can use to tell us how long an epoch takes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def epoch_time(start_time, end_time):\n",
    "    elapsed_time = end_time - start_time\n",
    "    elapsed_mins = int(elapsed_time / 60)\n",
    "    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n",
    "    return elapsed_mins, elapsed_secs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we train our actual model. This model is almost 3x faster than the convolutional sequence-to-sequence model and also achieves a lower validation perplexity!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ben/miniconda3/envs/pytorch17/lib/python3.8/site-packages/torchtext-0.9.0a0+c38fd42-py3.8-linux-x86_64.egg/torchtext/data/batch.py:23: UserWarning: Batch class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.\n",
      "  warnings.warn('{} class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.'.format(self.__class__.__name__), UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 01 | Time: 0m 13s\n",
      "\tTrain Loss: 4.222 | Train PPL:  68.172\n",
      "\t Val. Loss: 3.021 |  Val. PPL:  20.516\n",
      "Epoch: 02 | Time: 0m 13s\n",
      "\tTrain Loss: 2.813 | Train PPL:  16.657\n",
      "\t Val. Loss: 2.289 |  Val. PPL:   9.866\n",
      "Epoch: 03 | Time: 0m 13s\n",
      "\tTrain Loss: 2.236 | Train PPL:   9.358\n",
      "\t Val. Loss: 1.981 |  Val. PPL:   7.252\n",
      "Epoch: 04 | Time: 0m 13s\n",
      "\tTrain Loss: 1.889 | Train PPL:   6.613\n",
      "\t Val. Loss: 1.812 |  Val. PPL:   6.123\n",
      "Epoch: 05 | Time: 0m 13s\n",
      "\tTrain Loss: 1.644 | Train PPL:   5.177\n",
      "\t Val. Loss: 1.712 |  Val. PPL:   5.538\n",
      "Epoch: 06 | Time: 0m 14s\n",
      "\tTrain Loss: 1.458 | Train PPL:   4.296\n",
      "\t Val. Loss: 1.649 |  Val. PPL:   5.201\n",
      "Epoch: 07 | Time: 0m 13s\n",
      "\tTrain Loss: 1.311 | Train PPL:   3.710\n",
      "\t Val. Loss: 1.624 |  Val. PPL:   5.072\n",
      "Epoch: 08 | Time: 0m 13s\n",
      "\tTrain Loss: 1.185 | Train PPL:   3.269\n",
      "\t Val. Loss: 1.628 |  Val. PPL:   5.095\n",
      "Epoch: 09 | Time: 0m 14s\n",
      "\tTrain Loss: 1.074 | Train PPL:   2.928\n",
      "\t Val. Loss: 1.613 |  Val. PPL:   5.016\n",
      "Epoch: 10 | Time: 0m 14s\n",
      "\tTrain Loss: 0.978 | Train PPL:   2.659\n",
      "\t Val. Loss: 1.629 |  Val. PPL:   5.098\n"
     ]
    }
   ],
   "source": [
    "N_EPOCHS = 10\n",
    "CLIP = 1\n",
    "\n",
    "best_valid_loss = float('inf')\n",
    "\n",
    "for epoch in range(N_EPOCHS):\n",
    "    \n",
    "    start_time = time.time()\n",
    "    \n",
    "    train_loss = train(model, train_iterator, optimizer, criterion, CLIP)\n",
    "    valid_loss = evaluate(model, valid_iterator, criterion)\n",
    "    \n",
    "    end_time = time.time()\n",
    "    \n",
    "    epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n",
    "    \n",
    "    if valid_loss < best_valid_loss:\n",
    "        best_valid_loss = valid_loss\n",
    "        torch.save(model.state_dict(), 'tut6-model.pt')\n",
    "    \n",
    "    print(f'Epoch: {epoch+1:02} | Time: {epoch_mins}m {epoch_secs}s')\n",
    "    print(f'\\tTrain Loss: {train_loss:.3f} | Train PPL: {math.exp(train_loss):7.3f}')\n",
    "    print(f'\\t Val. Loss: {valid_loss:.3f} |  Val. PPL: {math.exp(valid_loss):7.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load our \"best\" parameters and manage to achieve a better test perplexity than all previous models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Test Loss: 1.671 | Test PPL:   5.316 |\n"
     ]
    }
   ],
   "source": [
    "model.load_state_dict(torch.load('tut6-model.pt'))\n",
    "\n",
    "test_loss = evaluate(model, test_iterator, criterion)\n",
    "\n",
    "print(f'| Test Loss: {test_loss:.3f} | Test PPL: {math.exp(test_loss):7.3f} |')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference\n",
    "\n",
    "Now we can can translations from our model with the `translate_sentence` function below.\n",
    "\n",
    "The steps taken are:\n",
    "- tokenize the source sentence if it has not been tokenized (is a string)\n",
    "- append the `<sos>` and `<eos>` tokens\n",
    "- numericalize the source sentence\n",
    "- convert it to a tensor and add a batch dimension\n",
    "- create the source sentence mask\n",
    "- feed the source sentence and mask into the encoder\n",
    "- create a list to hold the output sentence, initialized with an `<sos>` token\n",
    "- while we have not hit a maximum length\n",
    "  - convert the current output sentence prediction into a tensor with a batch dimension\n",
    "  - create a target sentence mask\n",
    "  - place the current output, encoder output and both masks into the decoder\n",
    "  - get next output token prediction from decoder along with attention\n",
    "  - add prediction to current output sentence prediction\n",
    "  - break if the prediction was an `<eos>` token\n",
    "- convert the output sentence from indexes to tokens\n",
    "- return the output sentence (with the `<sos>` token removed) and the attention from the last layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def translate_sentence(sentence, src_field, trg_field, model, device, max_len = 50):\n",
    "    \n",
    "    model.eval()\n",
    "        \n",
    "    if isinstance(sentence, str):\n",
    "        nlp = spacy.load('de_core_news_sm')\n",
    "        tokens = [token.text.lower() for token in nlp(sentence)]\n",
    "    else:\n",
    "        tokens = [token.lower() for token in sentence]\n",
    "\n",
    "    tokens = [src_field.init_token] + tokens + [src_field.eos_token]\n",
    "        \n",
    "    src_indexes = [src_field.vocab.stoi[token] for token in tokens]\n",
    "\n",
    "    src_tensor = torch.LongTensor(src_indexes).unsqueeze(0).to(device)\n",
    "    \n",
    "    src_mask = model.make_src_mask(src_tensor)\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        enc_src = model.encoder(src_tensor, src_mask)\n",
    "\n",
    "    trg_indexes = [trg_field.vocab.stoi[trg_field.init_token]]\n",
    "\n",
    "    for i in range(max_len):\n",
    "\n",
    "        trg_tensor = torch.LongTensor(trg_indexes).unsqueeze(0).to(device)\n",
    "\n",
    "        trg_mask = model.make_trg_mask(trg_tensor)\n",
    "        \n",
    "        with torch.no_grad():\n",
    "            output, attention = model.decoder(trg_tensor, enc_src, trg_mask, src_mask)\n",
    "        \n",
    "        pred_token = output.argmax(2)[:,-1].item()\n",
    "        \n",
    "        trg_indexes.append(pred_token)\n",
    "\n",
    "        if pred_token == trg_field.vocab.stoi[trg_field.eos_token]:\n",
    "            break\n",
    "    \n",
    "    trg_tokens = [trg_field.vocab.itos[i] for i in trg_indexes]\n",
    "    \n",
    "    return trg_tokens[1:], attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll now define a function that displays the attention over the source sentence for each step of the decoding. As this model has 8 heads our model we can view the attention for each of the heads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_attention(sentence, translation, attention, n_heads = 8, n_rows = 4, n_cols = 2):\n",
    "    \n",
    "    assert n_rows * n_cols == n_heads\n",
    "    \n",
    "    fig = plt.figure(figsize=(15,25))\n",
    "    \n",
    "    for i in range(n_heads):\n",
    "        \n",
    "        ax = fig.add_subplot(n_rows, n_cols, i+1)\n",
    "        \n",
    "        _attention = attention.squeeze(0)[i].cpu().detach().numpy()\n",
    "\n",
    "        cax = ax.matshow(_attention, cmap='bone')\n",
    "\n",
    "        ax.tick_params(labelsize=12)\n",
    "        ax.set_xticklabels(['']+['<sos>']+[t.lower() for t in sentence]+['<eos>'], \n",
    "                           rotation=45)\n",
    "        ax.set_yticklabels(['']+translation)\n",
    "\n",
    "        ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "        ax.yaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll get an example from the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "src = ['eine', 'frau', 'mit', 'einer', 'großen', 'geldbörse', 'geht', 'an', 'einem', 'tor', 'vorbei', '.']\n",
      "trg = ['a', 'woman', 'with', 'a', 'large', 'purse', 'is', 'walking', 'by', 'a', 'gate', '.']\n"
     ]
    }
   ],
   "source": [
    "example_idx = 8\n",
    "\n",
    "src = vars(train_data.examples[example_idx])['src']\n",
    "trg = vars(train_data.examples[example_idx])['trg']\n",
    "\n",
    "print(f'src = {src}')\n",
    "print(f'trg = {trg}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our translation looks pretty good, although our model changes *is walking by* to *walks by*. The meaning is still the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted trg = ['a', 'woman', 'with', 'a', 'large', 'purse', 'walks', 'by', 'a', 'gate', '.', '<eos>']\n"
     ]
    }
   ],
   "source": [
    "translation, attention = translate_sentence(src, SRC, TRG, model, device)\n",
    "\n",
    "print(f'predicted trg = {translation}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the attention from each head below. Each is certainly different, but it's difficult (perhaps impossible) to reason about what head has actually learned to pay attention to. Some heads pay full attention to \"eine\" when translating \"a\", some don't at all, and some do a little. They all seem to follow the similar \"downward staircase\" pattern and the attention when outputting the last two tokens is equally spread over the final two tokens in the input sentence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-30-8546ab0d7ba0>:16: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(['']+['<sos>']+[t.lower() for t in sentence]+['<eos>'],\n",
      "<ipython-input-30-8546ab0d7ba0>:18: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(['']+translation)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_attention(src, translation, attention)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's get an example the model has not been trained on from the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "src = ['ein', 'brauner', 'hund', 'rennt', 'dem', 'schwarzen', 'hund', 'hinterher', '.']\n",
      "trg = ['a', 'brown', 'dog', 'is', 'running', 'after', 'the', 'black', 'dog', '.']\n"
     ]
    }
   ],
   "source": [
    "example_idx = 6\n",
    "\n",
    "src = vars(valid_data.examples[example_idx])['src']\n",
    "trg = vars(valid_data.examples[example_idx])['trg']\n",
    "\n",
    "print(f'src = {src}')\n",
    "print(f'trg = {trg}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model translates it by switching *is running* to just *runs*, but it is an acceptable swap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted trg = ['a', 'brown', 'dog', 'runs', 'after', 'the', 'black', 'dog', '.', '<eos>']\n"
     ]
    }
   ],
   "source": [
    "translation, attention = translate_sentence(src, SRC, TRG, model, device)\n",
    "\n",
    "print(f'predicted trg = {translation}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, some heads pay full attention to \"ein\" whilst some pay no attention to it. Again, most of the heads seem to spread their attention over both the period and `<eos>` tokens in the source sentence when outputting the period and `<eos>` sentence in the predicted target sentence, though some seem to pay attention to tokens from near the start of the sentence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-30-8546ab0d7ba0>:16: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(['']+['<sos>']+[t.lower() for t in sentence]+['<eos>'],\n",
      "<ipython-input-30-8546ab0d7ba0>:18: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(['']+translation)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_attention(src, translation, attention)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we'll look at an example from the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "src = ['eine', 'mutter', 'und', 'ihr', 'kleiner', 'sohn', 'genießen', 'einen', 'schönen', 'tag', 'im', 'freien', '.']\n",
      "trg = ['a', 'mother', 'and', 'her', 'young', 'song', 'enjoying', 'a', 'beautiful', 'day', 'outside', '.']\n"
     ]
    }
   ],
   "source": [
    "example_idx = 10\n",
    "\n",
    "src = vars(test_data.examples[example_idx])['src']\n",
    "trg = vars(test_data.examples[example_idx])['trg']\n",
    "\n",
    "print(f'src = {src}')\n",
    "print(f'trg = {trg}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A perfect translation!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted trg = ['a', 'mother', 'and', 'her', 'young', 'son', 'enjoying', 'a', 'beautiful', 'day', 'outside', '.', '<eos>']\n"
     ]
    }
   ],
   "source": [
    "translation, attention = translate_sentence(src, SRC, TRG, model, device)\n",
    "\n",
    "print(f'predicted trg = {translation}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-30-8546ab0d7ba0>:16: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(['']+['<sos>']+[t.lower() for t in sentence]+['<eos>'],\n",
      "<ipython-input-30-8546ab0d7ba0>:18: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(['']+translation)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_attention(src, translation, attention)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BLEU\n",
    "\n",
    "Finally we calculate the BLEU score for the Transformer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchtext.data.metrics import bleu_score\n",
    "\n",
    "def calculate_bleu(data, src_field, trg_field, model, device, max_len = 50):\n",
    "    \n",
    "    trgs = []\n",
    "    pred_trgs = []\n",
    "    \n",
    "    for datum in data:\n",
    "        \n",
    "        src = vars(datum)['src']\n",
    "        trg = vars(datum)['trg']\n",
    "        \n",
    "        pred_trg, _ = translate_sentence(src, src_field, trg_field, model, device, max_len)\n",
    "        \n",
    "        #cut off <eos> token\n",
    "        pred_trg = pred_trg[:-1]\n",
    "        \n",
    "        pred_trgs.append(pred_trg)\n",
    "        trgs.append([trg])\n",
    "        \n",
    "    return bleu_score(pred_trgs, trgs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get a BLEU score of 36.52, which beats the ~34 of the convolutional sequence-to-sequence model and ~28 of the attention based RNN model. All this whilst having the least amount of parameters and the fastest training time!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BLEU score = 36.52\n"
     ]
    }
   ],
   "source": [
    "bleu_score = calculate_bleu(test_data, SRC, TRG, model, device)\n",
    "\n",
    "print(f'BLEU score = {bleu_score*100:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations for finishing these tutorials! I hope you've found them useful.\n",
    "\n",
    "If you find any mistakes or want to ask any questions about any of the code or explanations used, feel free to submit a GitHub issue and I will try to correct it ASAP.\n",
    "\n",
    "## Appendix\n",
    "\n",
    "The `calculate_bleu` function above is unoptimized. Below is a significantly faster, vectorized version of it that should be used if needed. Credit for the implementation goes to [@azadyasar](https://github.com/azadyasar)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def translate_sentence_vectorized(src_tensor, src_field, trg_field, model, device, max_len=50):\n",
    "    assert isinstance(src_tensor, torch.Tensor)\n",
    "\n",
    "    model.eval()\n",
    "    src_mask = model.make_src_mask(src_tensor)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        enc_src = model.encoder(src_tensor, src_mask)\n",
    "    # enc_src = [batch_sz, src_len, hid_dim]\n",
    "\n",
    "    trg_indexes = [[trg_field.vocab.stoi[trg_field.init_token]] for _ in range(len(src_tensor))]\n",
    "    # Even though some examples might have been completed by producing a <eos> token\n",
    "    # we still need to feed them through the model because other are not yet finished\n",
    "    # and all examples act as a batch. Once every single sentence prediction encounters\n",
    "    # <eos> token, then we can stop predicting.\n",
    "    translations_done = [0] * len(src_tensor)\n",
    "    for i in range(max_len):\n",
    "        trg_tensor = torch.LongTensor(trg_indexes).to(device)\n",
    "        trg_mask = model.make_trg_mask(trg_tensor)\n",
    "        with torch.no_grad():\n",
    "            output, attention = model.decoder(trg_tensor, enc_src, trg_mask, src_mask)\n",
    "        pred_tokens = output.argmax(2)[:,-1]\n",
    "        for i, pred_token_i in enumerate(pred_tokens):\n",
    "            trg_indexes[i].append(pred_token_i)\n",
    "            if pred_token_i == trg_field.vocab.stoi[trg_field.eos_token]:\n",
    "                translations_done[i] = 1\n",
    "        if all(translations_done):\n",
    "            break\n",
    "\n",
    "    # Iterate through each predicted example one by one;\n",
    "    # Cut-off the portion including the after the <eos> token\n",
    "    pred_sentences = []\n",
    "    for trg_sentence in trg_indexes:\n",
    "        pred_sentence = []\n",
    "        for i in range(1, len(trg_sentence)):\n",
    "            if trg_sentence[i] == trg_field.vocab.stoi[trg_field.eos_token]:\n",
    "                break\n",
    "            pred_sentence.append(trg_field.vocab.itos[trg_sentence[i]])\n",
    "        pred_sentences.append(pred_sentence)\n",
    "\n",
    "    return pred_sentences, attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchtext.data.metrics import bleu_score\n",
    "\n",
    "def calculate_bleu_alt(iterator, src_field, trg_field, model, device, max_len = 50):\n",
    "    trgs = []\n",
    "    pred_trgs = []\n",
    "    with torch.no_grad():\n",
    "        for batch in iterator:\n",
    "            src = batch.src\n",
    "            trg = batch.trg\n",
    "            _trgs = []\n",
    "            for sentence in trg:\n",
    "                tmp = []\n",
    "                # Start from the first token which skips the <start> token\n",
    "                for i in sentence[1:]:\n",
    "                    # Targets are padded. So stop appending as soon as a padding or eos token is encountered\n",
    "                    if i == trg_field.vocab.stoi[trg_field.eos_token] or i == trg_field.vocab.stoi[trg_field.pad_token]:\n",
    "                        break\n",
    "                    tmp.append(trg_field.vocab.itos[i])\n",
    "                _trgs.append([tmp])\n",
    "            trgs += _trgs\n",
    "            pred_trg, _ = translate_sentence_vectorized(src, src_field, trg_field, model, device)\n",
    "            pred_trgs += pred_trg\n",
    "    return pred_trgs, trgs, bleu_score(pred_trgs, trgs)"
   ]
  }
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